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تصفح المحتوي RDA
التصفح حسب الموضوعات
التصفح حسب اللغة
التصفح حسب الناشر
التصفح حسب تاريخ النشر
التصفح حسب مكان النشر
التصفح حسب المؤلفين
تصفح الهيئات
التصفح المؤتمرات
التصفح حسب نوع المادة
التصفح حسب العلاقة بالعمل
تم العثور علي : 3291
 تم العثور علي : 3291
  
 
إعادة البحث

Thesis 2024.
Elshair ‘ Amira Bushra Ali Hassan .
900 الجغرافيا والرحلات والتراجم والتاريخ   900 الجغرافيا والرحلات والتراجم والتاريخ   900 الجغرافيا والرحلات والتراجم والتاريخ  
This research aimed to extend prior literature that focuses on the impact of comprehensive
income presentation formats on investors’ usefulness. It examined the risk relevance of
comprehensive income volatility on a sample of 74 non-financial publicly traded firms - listed
on the Egyptian Stock of Exchange - over the period from 2014 to 2019. Two years (2014 &
2015) before the implementation of adjustments to the Egyptian Accounting Standard No. (1)
issued in 2015 - and four years after the implementation (2016-2019). The findings revealed
that comprehensive income volatility and the incremental volatility of comprehensive income
over net income provide risk-relevant information regarding the firm’s total equity risk -
whereas none of the income volatility measures captures any relevant information concerning
firms’ market risk. The findings suggest that comprehensive income volatility captures risk
factors not reflected in net income volatility. Moreover - investors in the Egyptian stock market
are affected by the “Format” of presenting comprehensive income - which implies that the
Egyptian stock market is not efficient under a semi-strong form. Investors incorporate
comprehensive income volatility and the incremental volatility of comprehensive income over
net income into equity valuation only when comprehensive income is reported saliently in a
separate statement. The findings conform with the “bounded rationality” - “incomplete
revaluation hypothesis” and “the limited attention hypothesis”. In addition - applying
international financial reporting standards (IFRS) enhances the efficiency of the Egyptian
capital market.
Keywords: Comprehensive Income Volatility - Net Income Volatility - Market Risk - Equity
Risk - Stock Prices - Presentation Format - Risk-Relevance - Equity Valuation

Articles 2024
Hussein, Mohamed F.
Endocrine-disrupting chemicals - Environmental risk factors
Journal of High Institute of Public Health/
Vo.54, No.1(April 2024) /
University of Alexandria.High Institute of Public Health,

Articles 2022
Wahdan, Mohamed Ali.
658 إدارة الأعمال العامة   658.4 أسس الإدارة   658.403 اتخاذ القرار  
Management information systems - Enterprise resource planning - Business enterprises - Business planning - Auditing - Auditing - Information technology
مجلة الدراسات والبحوث المحاسبية
مج. 4، ع. 2 (ديسمبر 2022) /
جامعة بنها، كلية التجارة، قسم المحاسبة،

Articles 2022
Ahmed, Tarek Abd El-Azeem.
658 إدارة الأعمال العامة  
Strategic alliances (Business) - Strategic alliances (Business) - Auditing - Accounting firms - Auditing
مجلة الدراسات والبحوث المحاسبية
مج. 4، ع. 2 (ديسمبر 2022) /
جامعة بنها، كلية التجارة، قسم المحاسبة،

Articles 2022
Abu Mousa, Ahmed Abdel-Salam.
658 إدارة الأعمال العامة   658.05 جمع و تبويب المعلومات  
Information technology - Artificial intelligence - Risk management - Information technology
مجلة الدراسات والبحوث المحاسبية
مج. 4، ع. 2 (ديسمبر 2022) /
جامعة بنها، كلية التجارة، قسم المحاسبة،

Thesis 2024
Idris,Mahmoud Gamal El Sayed
617 الجراحة والموضوعات ذات الصلة  
تصفح الوعاء  

Thesis 2024
Elshouky ‘ Hadeer Hussein Abdel-Mawla Hassan .
621 الفيزياء التطبيقية   621.3 الهندسة الكهربية  
The use of image classification in medical fields is one of the most important uses - including skin cancer image classification. Skin cancer is a major health problem across the world - and early identification is critical for successful treatment. Skin cancer - which is defined by abnormal skin cell development - is a common and dangerous disease worldwide. Despite advances in digital diagnosis tools - many present skin cancer detection technologies frequently fail to attain adequate levels of accuracy. Disease detection - computer-aided diagnosis - and patient risk identification rely heavily on computer vision. This is particularly true for skin cancer - which may be lethal if not detected early on. Several computer-aided diagnosis and detection systems have already been developed to do this.
In this dissertation - two approaches for classifying skin cancer images were examined and compared with the proposed methods. Machine Learning (ML) and Deep Learning (DL) are these two approaches. ML approaches include Artificial Neural Networks - Support Vector Machines - Naïve Bayes - and Decision Tree. Both Convolutional Neural Networks and Pretrained Deep Neural Networks (PDNN) were employed in the DL approach.
Two methods for detecting and binary classifying dermoscopic skin cancer images into benign and malignant were proposed. The first proposed method employs K-Nearest Neighbor (KNN) as a classifier with several PDNN serving as feature extractors - (KNN-PDNN). These networks include AlexNet - VGG-16 - VGG-19 - EfficientNet-B0 - ResNet-18 - ResNet-50 - ResNet-101 - DenseNet-201 - Inception-v3 - and MobileNet-v2. The second proposed method employs some PDNN with the Improved Grey Wolf Optimizer (I-GWO) - (PDNN-I-GWO). The PDNN used in this technique are AlexNet - ResNet-18 - SqueezeNet - ShuffleNet - and DarkNet-19.
The experiments of KNN-PDNN method used 4000 images from the ISIC archive dataset to train and test images. In certain PDNN - the KNN-PDNN method’s accuracy exceeded 99%. The PDNN-I-GWO method investigated two datasets: MED-NODE and DermIS. The outcomes showed that the proposed methods outperformed the other tested approaches. The highest accuracy achieved by this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively. The highest accuracy achieved with this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively.
The dissertation consists of five chapters as follows:
Chapter 1: Introduction
An introduction to the dissertation is given - explaining the importance of the research point and the goals it seeks to achieve - and an explanation of the problems found in some of the old techniques that we seek to improve in this thesis and the extent of their impact on classifying skin cancer images. This chapter also summarizes what the other chapters contain and the order in which they are reviewed in the thesis.
Chapter 2: Literature Review
This chapter covers background on skin cancer image classification and presents some previous works and methods used and their features and characteristics.
Chapter 3: Proposed System
The third chapter presents the proposed algorithms that were represented and applied in the dissertation. It reviews them in detail and discusses the additions and modifications that were made to achieve high accuracy. This chapter also presents the preprocessing of images before using them in the proposed methods. In addition - it includes different datasets for training and testing images.
Chapter 4: Experimental Results
It reviews all the experiments - their accompanying results - and details of the images that were used in the experiments. This dissertation also includes many comparisons between the proposed and modified algorithms that were used during the image classification process. This included using several methods and methods to evaluate and compare the performance of these algorithms.
Chapter 5: Conclusions and Recommendations for Future Work
It presents a summary of the results reached as well as some recommended points for future work that can be used to develop the work presented in this dissertation or related works - The use of image classification in medical fields is one of the most important uses - including skin cancer image classification. Skin cancer is a major health problem across the world - and early identification is critical for successful treatment. Skin cancer - which is defined by abnormal skin cell development - is a common and dangerous disease worldwide. Despite advances in digital diagnosis tools - many present skin cancer detection technologies frequently fail to attain adequate levels of accuracy. Disease detection - computer-aided diagnosis - and patient risk identification rely heavily on computer vision. This is particularly true for skin cancer - which may be lethal if not detected early on. Several computer-aided diagnosis and detection systems have already been developed to do this.
In this dissertation - two approaches for classifying skin cancer images were examined and compared with the proposed methods. Machine Learning (ML) and Deep Learning (DL) are these two approaches. ML approaches include Artificial Neural Networks - Support Vector Machines - Naïve Bayes - and Decision Tree. Both Convolutional Neural Networks and Pretrained Deep Neural Networks (PDNN) were employed in the DL approach.
Two methods for detecting and binary classifying dermoscopic skin cancer images into benign and malignant were proposed. The first proposed method employs K-Nearest Neighbor (KNN) as a classifier with several PDNN serving as feature extractors - (KNN-PDNN). These networks include AlexNet - VGG-16 - VGG-19 - EfficientNet-B0 - ResNet-18 - ResNet-50 - ResNet-101 - DenseNet-201 - Inception-v3 - and MobileNet-v2. The second proposed method employs some PDNN with the Improved Grey Wolf Optimizer (I-GWO) - (PDNN-I-GWO). The PDNN used in this technique are AlexNet - ResNet-18 - SqueezeNet - ShuffleNet - and DarkNet-19.
The experiments of KNN-PDNN method used 4000 images from the ISIC archive dataset to train and test images. In certain PDNN - the KNN-PDNN method’s accuracy exceeded 99%. The PDNN-I-GWO method investigated two datasets: MED-NODE and DermIS. The outcomes showed that the proposed methods outperformed the other tested approaches. The highest accuracy achieved by this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively. The highest accuracy achieved with this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively.
The dissertation consists of five chapters as follows:
Chapter 1: Introduction
An introduction to the dissertation is given - explaining the importance of the research point and the goals it seeks to achieve - and an explanation of the problems found in some of the old techniques that we seek to improve in this thesis and the extent of their impact on classifying skin cancer images. This chapter also summarizes what the other chapters contain and the order in which they are reviewed in the thesis.
Chapter 2: Literature Review
This chapter covers background on skin cancer image classification and presents some previous works and methods used and their features and characteristics.
Chapter 3: Proposed System
The third chapter presents the proposed algorithms that were represented and applied in the dissertation. It reviews them in detail and discusses the additions and modifications that were made to achieve high accuracy. This chapter also presents the preprocessing of images before using them in the proposed methods. In addition - it includes different datasets for training and testing images.
Chapter 4: Experimental Results
It reviews all the experiments - their accompanying results - and details of the images that were used in the experiments. This dissertation also includes many comparisons between the proposed and modified algorithms that were used during the image classification process. This included using several methods and methods to evaluate and compare the performance of these algorithms.
Chapter 5: Conclusions and Recommendations for Future Work
It presents a summary of the results reached as well as some recommended points for future work that can be used to develop the work presented in this dissertation or related works

Thesis 2024.
Taha, Amal Mahmoud.
618 أمراض النساء والولادة والموضوعات ذات الصلة  
Preeclampsia - Preeclampsia - Preeclampsia
تصفح الوعاء  

Thesis 2024.
Hassan, Ahmed Abd El-Hamed.
616 الأمراض   616.3 أمراض الجهاز الهضمي  
Obesity - Heart - Body mass index
تصفح الوعاء  

Thesis 2024.
Eltabakh, Elsaeed Hussein.
616 الأمراض  
Angina pectoris - Myocardial infarction - Cardiovascular Diseases - Angina Pectoris - Coronary Artery Disease
تصفح الوعاء  


من 330
 







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